Locally Informed Simulation to Predict Hospital Capacity Needs During the COVID-19 Pandemic.

ABSTRACT:

Background

The coronavirus disease 2019 (COVID-19) pandemic challenges hospital leaders to make time-sensitive, critical decisions about clinical operations and resource allocations.

Objective

To estimate the timing of surges in clinical demand and the best- and worst-case scenarios of local COVID-19-induced strain on hospital capacity, and thus inform clinical operations and staffing demands and identify when hospital capacity would be saturated.

Design

Monte Carlo simulation instantiation of a susceptible, infected, removed (SIR) model with a 1-day cycle.

Setting

3 hospitals in an academic health system.

Patients

All people living in the greater Philadelphia region.

Measurements

The COVID-19 Hospital Impact Model (CHIME) (http://penn-chime.phl.i

SUBMITTER: Weissman GE 

PROVIDER: S-EPMC7153364 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature